基于IDEPSO-SS的多工况转向架构架可靠性分析

基于子集模拟(Subset Simulation,SS)方法求解结构失效概率,再采用改进差分进化粒子群算法(IDEPSO)优化每层样本个数和初始条件概率,以获得单工况下结构最优失效概率,并结合几何分析确定联合失效概率,计算多工况下结构失效概率,确定最优失效次序.以某型转向架构架为研究对象,先采用SS方法建立构架各工况下...     

基于非集计模型的统计生命价值分析

统计生命价值是进行交通项目成本效益分析的重要参数。首先分析比较统计生命价值的不同计算方法,然后在理论上论证使用非集计模型来导出统计生命价值的可行性,最后运用正交试验法设计交通意愿调查问卷,为今后进一步收集数据、建立路径选择模型、计算统计生命价值奠定基础。     

桥梁设计方案风险评估分析

桥梁结构风险贯穿于桥梁建设全过程.主要介绍桥梁在方案设计阶段的风险评估方法及其主要评估流程,以实际工程为例,阐述采用专家调查法确定桥梁设计方案的安全风险水平,并采用基于估计相对位置的方案排队法优选出较优方案,为桥梁方案设计阶段的风险评估提供参考依据.     

Numerical evaluation method of lightning rod protection probability北大核心CSCD

［Objectives］Aiming at the current situation in which it is difficult to efficiently evaluate protection probability through traditional lightning rod evaluation methods, an efficient numerical evaluation algorithm is developed on the basis of an electrogeometric model (EGM) and attractive volume to realize the efficient calculation of lightning protection probability at any point in space.［Methods］This method first determines the attractive volume boundary of the lightning rod and protection object according to the interception process of the upward and downward leaders. The collection surface and exposure arc of the lightning stroke distance are then calculated, enabling the attractive risk and interception effect of the lightning rod to be quantified. Finally, the attraction and interception characteristics of the lightning rod are integrated to establish a numerical evaluation model of protection probability. To verify the accuracy of this method, the general rule of lightning rod protection probability is analyzed and the results compared with the existing analysis method.［Result］The evaluation results of this method show good agreement with those of classical leader progression model (LPM) theory.［Conclusions］ The method proposed herein has a high degree of quantification and can realize the efficient calculation of lightning protection probability at any point in space, which can provide useful references for lightning protection design work. © 2023 Authors. All rights reserved.     

Required test durations for converged short-term wave and impact extreme value statistics — Part 1: Ferry dataset

For the design of maritime structures in waves, the extreme values of responses such as motions and wave impact loads are required. Waves and wave-induced responses are stochastic, so such responses should always be related to a probability. This information is not easy to obtain for strongly non-linear responses such as wave impact forces. Usually class rules or direct assessment via experiments or numerical simulations are applied to obtain extreme values for design. This brings up questions related to the convergence of extreme values: how long do we need to test in order to obtain converged statistics for the target duration? Or, vice versa: given testing data, what is the uncertainty of the associated statistics? Often the test or simulation duration is cut up in ‘seeds’ or ‘realisations’, with an exposure duration of one or three hours based on the typical duration of a steady environmental condition at sea, or the time that a ship sails a single course. The required number of seeds for converged results depends on the type of structure and response, the exposure duration, and the desired probability level. The present study provides guidelines for the convergence of most probable maximum (MPM) wave crest heights and MPM green water wave impact forces on a ferry. Long duration experiments were done to gain insight into the required number of seeds, and the effect of fitting. The present paper presents part 1 of this study; part 2 [1] presents similar results for wave-in-deck loads on a stationary deck box.     

Required test durations for converged short-term wave and impact extreme value statistics–Part 2: Deck box dataset

In the assessment of wave-in-deck loads for new and existing maritime structures typically model tests are carried out. To determine the most critical conditions and measure sufficient impact loads, a range of sea states and various seeds (realisations) for each sea state are tested. Based on these measurements, probability distributions can be derived and design loads determined. In air gap model testing usually only few, if any, impact loads occur per 3-hour seed. This can make it challenging to derive reliable probability distributions of the measured loads, especially when only a few seeds are generated. In addition wave impact forces, such as greenwater loading, slamming, or air gap impacts are typically strongly non-linear, resulting in a large variability of the measured loads. This results in the following questions: How many impacts are needed to derive a reliable distribution? How is the repeatability of individual events affecting the overall distribution? To answer these questions wave-in-deck model tests were carried out in 100 x 3-hour realisations of a 10,000 year North Sea sea state. The resulting probability distributions of the undisturbed wave measurements as well as the measured wave-in-deck loads are presented in this paper with focus on deriving the number of seeds and exposure durations required for a reliable estimate of design loads.The presented study is Part 2 of a combined study on guidance for the convergence and variability of wave crests and impact loading extreme values. The data set of Part 1 ([1]) is based on greenwater loads on a sailing ferry and the data set of Part 2 on wave-in-deck loads on a stationary deck box.     

Fatigue assessment of ship structures based on equivalent wave probability (EWP) concept (1st report): Proposal of EWP concept and its verification by 8600TEU container ship's onboard hull monitoring

A long-term fatigue assessment method based on EWP concept is proposed. ‘Equivalent wave probability (EWP)’ is the fictitious (H_S, T_m)'s joint probability distribution function (JPDF), for which the frequency distribution of the stress variance R², f(R²), calculated by spectral fatigue assessment agrees with the observed one. By choosing probability function p(R²) to fit f(R²), the R²'s statistical model (R2SM) which represents the relation between the EWP parameters and R²'s population parameters is developed, and the Bayesian inference, which can estimate the EWP parameters from the measured R² data is developed. The EWP at the reference position (RP) can be determined by Bayesian inference from the measured R² through the R2SM at RP. To accurately estimate the measured f(R²) at the target position (TP) from the EWP at RP, an R2SM correction factor at TP, denoted by α_TP, is introduced in the process of assimilating R2SM. The resulting R2SM, which has been assimilated by Bayesian inference using measured data, is referred to as data-assimilated R2SM (DAR2SM). The fatigue assessment using EWP at RP as the input of DAR2SM at TP is called Bayes-EWP-DAR2SM analysis. The validity of Bayes-EWP-DAR2SM analysis is verified by using the long-term (about four years) multi(12)-position hull monitoring (HM) data of an 8,600TEU container ship. The fatigue damages estimated by Bayes-EWP-DAR2SM based solely on the stress history of a single sensor are in agreement with measurements with sufficient accuracy, independent of the chosen data assimilation period. This demonstrates that the multi-position fatigue assessment solely through HM at one RP based on EWP concept is realized.